MKTG 662 Marketing Analytics

Questions

Answers

1. The article gives a brief overview about exploring the use of analytics and big data in marketing. There is also discussion about the increasing importance of big data and analytics in marketing. However, the article put forward a clear definition of big data and analytics that enables one to understand its defining and unique characteristics. The article also describes that the advancement in technology has enabled us to get hold of rich and plentiful consumer data based on real time. Big data therefore enables one to understand the impact on various marketing activities. However, as per the article three resources are responsible for moderating the collection of data that are organizational, human and physical.

The article defines analytics. Further, analytics described as a multidimensional field that uses predictive modelling, machine learning techniques, statistics and mathematics for finding meaningful patterns of recorded data. The article also focuses that business organizations use analytics on business data in order to predict, describe and improve their performance.  Analytics needs huge amount of computation so the software and algorithm meant for the purpose use the most updated methods (Gandomi  & Haider 2015)

The article also discusses about marketing that has become a data driven process from a creative process. By going through the article one can understand that most organizations use analytics for determination of the outcomes of campaigns to get guided decision for targeting consumers and investment decisions. There has been description about the type of analytics used. One can also get an overview as to the techniques of analytics marketers use. The key learning from the article is also discussed with respect to the marketing strategy and planning. There is also a section in the article that discusses about writer’s take on whether marketing analytics be used as an approach by major corporations for selling packaged consumer goods

2. Great marketing though believed to be a form of art but nowadays, gradually big data and analytics is making a serious entry in the arena. Analytics refers to the communication, interpretation and discovery of sensible data patterns. The most common type of analytics in use is descriptive analysis, predictive analysis and prescriptive analysis. However, marketers use various techniques of analytics like the demographic studies, conjoint analysis, customer segmentation to study huge amount of customer purchase, survey and panel data for better understanding and communicating marketing strategy

On the other hand, big data described as high velocity, high volume and greater value assets that demand forms of information processing that are not only cost effective but also innovative. Further, they help in better decision-making and insight (Gandomi & Haider, 2015).

The most common type of analytics in use is descriptive analysis, predictive analysis and prescriptive analysis. However, marketers use various techniques of analytics like the demographic studies, conjoint analysis, customer segmentation to study huge amount of customer purchase, survey and panel data for better understanding and communicating marketing strategy

There are smart marketers who lay more trust on data than before to test, inform and categorize their strategies. However, data and analytics can never be a replacement to the creative minds that are responsible for marketing campaign but they surely do provide insights and tools that enable them to perform better. Usage of data and analytics allows a marketer to define their ideal customer and get the details about their customers. Big data also helps marketers in optimization of customer engagement thereby providing an insight into the type and the frequency of purchase. This leads to an increase in customer loyalty and retention. In addition to this, companies can optimize their buys across various channels and get better allocation of their marketing budgets for better return on investment. The big data and analytics also aid small businesses with tools to keep a track of the content necessary for closing the sales. The procedure has also made quantitative and qualitative analysis of companies much easier. Companies can also monitor the presence of their brands through various social channels and websites to find various reviews, testimonials and opinions but also have competitive advantage (Kapferer, 2012).

Analytics and big data are responsible for understanding the credibility of activities in the market. Through this procedure, marketing programs analyzed in the light to optimize return on investment, improvement of efficiencies and spending (Evans & Lindner 2012). Therefore, analytics is useful in selecting the marketing activities that are more efficient, choosing the most effective media channels and the segments of the customer and product line that creates more profit.  Moreover, the usage of analytics and big data is not only a benchmark for present activity but also is a measure of past performance. The present activities include tracking the performance of the campaign, the channels that are at work and the segment of the customers that are responding. Analytics and big data allow a deep insight into the behaviour of the competitor, customer as well as the market. In this regard, if deployed strategically it can enable an organization to enhance its effectiveness and tangibility.

Nowadays, an increasing number of organizations are making use of business analytics to generate strategies for revenue generation against cost containment. In this context, research have found that close to 40% companies see an increasing investment return within a period of 6 months of adoption of analytics and bid data. However, for an organization there are three factors important for advancing business analytics that include trust, sponsorship and skills. However, the leaders of analytics address these gaps to enhance in depth skills and knowledge, executive advocacy and enhance trust (Minelli, Chambers & Dhiraj, 2012).

3. Key Learning:

However, in marketing, analytics and big data gives an insight to the most important content that is effective in each level of a sales cycle, improvement in customer relationship management, strategies for prospect engagement, increasing conversion rates, revenue and lifetime value of customers. In the contrary, for cloud based software companies, the insight through big data and analytics is on lowering customer acquisition cost, managing customer driven metrics for running the business and lowering customer lifetime value(Chen, Chiang & Storey, 2012).

In this regard, the key learning from the article described below:

• The big data and analytics helps differentiating price strategies at product-customer level and make optimizing pricing an achievable option. In this regard, companies are not only gaining greater response from customers but are also gaining their insights.
• Thus big data is playing a big role in revolutionizing companies. In sales and marketing, the most popular data use cases include 48% customer analytics, 21% operational analytics, 10% enterprise data warehouse optimization, 12% fraud compliance, 10% service innovation and new product.
• Analytics and big data have now enabled contextual marketing through embedded intelligence. The marketer’s ability to go beyond the execution of a campaign and thereby enable further successful customer relationships made possible by big data analytics.
• Many industries are now relying on big data and analytics to optimize selling strategies and use geoanalytics to make go to market plans. However, it has been found that mobile and email marketing, marketing and search engine optimization is the place where big data and analytics is largely having its impact.

4. Most companies that sell consumer packaged goods adopt marketing analytics for its numerous benefits. Through product development and portfolio strategy companies cannot only understand the attitudes and needs of the consumers but also target opportunities of highest value by identifying consumer segments (Moorman et al., (2012).  The consumers can easily measure the return on investments on marketing across both modern and traditional vehicles of marketing thereby enabling them to choose the most effective mechanism. The stores can get an idea about their in stock rates through time-to-time analysis.  The analysis also enhances shopping experience of consumers by reducing things out of stock and thereby boosting up sales for both their partners and themselves. Therefore, a consumer product goods company applies advanced analytics on consumer data to revolutionize its assortments that are retailer specific. Thus, companies that ensure analytics and big data into their operations have moved ahead of their peers by 5% and enhanced profitability by 6%.

The article concludes in the light that analytics and big data are an important aspect in determining the position of a company. Here one can see how depending on analytics and big data can be beneficial to a company in the long-run. Further, there also discussion about the need of using analytics and data in an organization. The key learning point one can have through these methods discussed. There is also writer’s take on the importance of analytics and big data in companies that sell mostly consumer packaged goods. Thus, the marketing function is steered effectively with a well- structured data and analytic strategy.

References:

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4).

Cooper, A. (2012). What is analytics? Definition and essential characteristics. CETIS Analytics Series, 1(5), 1-10.

Evans, J. R., & Lindner, C. H. (2012). Business analytics: The next frontier for decision sciences. Decision Line, 43(2), 4-6.

Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.

Kapferer, J. N. (2012). The new strategic brand management: Advanced insights and strategic thinking. Kogan page publishers.

Minelli, M., Chambers, M., & Dhiraj, A. (2012). Big data, big analytics: emerging business intelligence and analytic trends for today's businesses. John Wiley & Sons.

Moorman, C., Wies, S., Mizik, N., & Spencer, F. J. (2012). Firm innovation and the ratchet effect among consumer packaged goods firms. Marketing Science, 31(6), 934-951.

Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51-59.

Sagiroglu, S., & Sinanc, D. (2013, May). Big data: A review. In Collaboration Technologies and Systems (CTS), 2013 International Conference on (pp. 42-47). IEEE.